Discrimination of five commercial Guibourtia wood species using terahertz time domain spectroscopy combined with machine learning approaches

Abstract Terahertz waves hold significant potential for applications in wood identification, owing to their good penetration and distinctive fingerprints in wood. This study focuses on wood samples from five different Guibourtia species as the research objects. The terahertz time-domain spectroscopy (THz-TDS) is employed to acquire the spectroscopic signals of the wood samples and to extract their optical parameter data. The THz refractive indices are dimensionally reduced through principal component analysis (PCA), and three machine learning models, namely partial least squares-discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM), are employed to classify the wood of five different Guibourtia species. Time delays of the wood samples from five different Guibourtia species are concentrated in the range of 60–62 ps and exhibit different amplitudes in the frequency domain. Refractive indices showed significant variations within the THz band. PCA for dimensionality reduction of terahertz time-domain spectral data significantly improves the recognition rate of machine learning models. Applying PCA to the refractive index data, the RF model achieves a highest recognition rate of 96.9 % and an overall classification accuracy of 98 %. Current results demonstrate that THz-TDS enables rapid, accurate, and non-destructive classification and identification of wood from the Guibourtia species.